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Record W4387741537 · doi:10.30688/janzssa.2023-2-08

Examining the Role of Faculty Subcultures in Perceptions of Student Retention Initiatives

2023· report· en· W4387741537 on OpenAlexaff
Christine Arnold, Kathleen Clarke, Tricia A. Seifert

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsWilfrid Laurier UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsPerceptionInstitutionDisciplineStudent affairsPublic relationsPolitical scienceAcademic institutionMedical educationHigher educationPsychologyManagementMedicine

Abstract

fetched live from OpenAlex

Scholars and practitioners have argued that student success must be a shared responsibility among members of the campus community. Academic and student affairs cultures play imperative roles in the establishment and success of partnerships designed to support student success. However, little is known about the differences within the academic affairs culture that shapes faculty members’ perceptions of such initiatives. Understanding how faculty members perceive student retention efforts is essential in developing a shared responsibility for student success. This research examines the extent to which faculty with various academic ranks (tenured/promoted, tenure track, and non-tenure track/non-promotional), years employed at current institution, and broad disciplinary areas vary in their perceptions of departmental and institutional retention initiatives. Faculty members’ perceptions of these retention initiatives are measured according to awareness of their departments’ and institutions’ academic and co-curricular activities, dedication of resources towards promoting retention, and communication about available support services. Results revealed variations among faculty members in their perceptions of departmental and institutional retention efforts according to the subcultures analysed. Implications for faculty members, student affairs staff members, and administration are considered.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.314
GPT teacher head0.542
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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